(1) Field of Invention
The present invention is related to a blind source separator and, more particularly, to a cognitive blind source separator that is operable for separating multiple temporally correlated source radio-frequency (RF) signals over an ultra-wide bandwidth (e.g., >30 gigahertz (Ghz)).
(2) Description of Related Art
Blind signal separation, also known as blind source separation, is the separation of a set of source signals from a set of mixed signals (mixture signals), without the aid of information (or with very little information) about the source signals or the mixing process. Thus, in the Blind Source Separation (BSS) problem, any single antenna measures multiple source signals. There may be more than one antenna measuring the signals, but in general each antenna “sees” all of the source signals and creates a different linear mixture of them. The task is then to use the measured mixture signals in order to recover the original source signals. The case of a single antenna operating in isolation is especially challenging because there is no sense of spatial resolution to aid in the extraction process.
Conventional methods for BSS typically require a greater number of input mixtures (which maps directly to a greater number of antenna) than the number of source signals, limiting their applicability in size-, weight-, and power (SWaP)-constrained scenarios (see, for example, the List of Incorporated Literature References, Literature Reference No. 1). Some extensions to conventional BSS have addressed the “underdetermined” scenario (with fewer mixtures than sources) that leverage prior knowledge about the sources, such as having “low complexity” or having a sparse representation with respect to a learned dictionary. Such models of prior knowledge are too broad, enabling the system to over fit an entire mixture as a single source, and require large amounts of memory to store the dictionary and computation to recover the presentation of the input mixtures with respect to the dictionary (see, for example, Literature Reference Nos. 1 and 3). In other work, the researchers coupled the BSS algorithm with an infinite impulse response (IIR) bandpass filter with tunable center frequency in order to separate temporally correlated sources (see Literature Reference No. 2). This work was still quite limited, requiring at least as many mixtures as sources, requiring that the mixtures be “prewhitened” to have an identity-valued covariance matrix, and using the second-order statistics of sources as the sole cue for separation.
It should be noted that such source signals are often transmitted as radio-frequency (RF) signals over an ultra-wide bandwidth. State-of-the-art systems for detecting, localizing, and classifying source emitters from passive RF antennas over an ultra-wide bandwidth (>30 gigahertz (Ghz)) require high rate analog-to-digital converters (ADC). Such high-rate ADCs are expensive and power hungry, and due to fundamental physical limits (such as the Walden curve as described in Literature Reference No. 5), are not capable of achieving the sampling rate needed to capture the ultra-wide bandwidth. To mitigate this, service oriented architecture (SOA) electronic support measure (ESM) systems use either spectrum sweeping (which is too slow to handle agile emitters) or a suite of digital channelizers, which have large size, weight, and power requirements. In addition, the detection, localization, and classification algorithm that SOA ESM systems use are typically based on the fast Fourier transform, with high computational complexity and memory requirements that make it difficult to operate in real-time over an ultra-wide bandwidth.
Thus, a continuing need exists for a blind source separator that is operable for separating multiple temporally correlated source RF signals over an ultra-wide bandwidth (e.g., >30 Ghz) using as little as a single antenna.